Generative AI Architect- Manager

What matters most

• Architecting Scalable Agentic Systems

  The core requirement is to design and deploy enterprise-grade generative AI solutions that utilize multi-agent orchestration and autonomous task execution. You must demonstrate the ability to move beyond simple RAG implementations toward complex, tool-using agentic architectures that are production-ready, scalable, and cost-efficient.

• Establishing LLMOps and Observability Standards

  You are expected to define the operational framework for AI projects, including telemetry, monitoring, and automated testing. This involves implementing robust CI/CD pipelines and observability tools like LangSmith or Langfuse to ensure that agentic systems meet strict production SLAs and reliability requirements.

• Technical Leadership and Mentorship

  As a Manager, you must lead engineering teams through the full project lifecycle. This includes conducting architecture reviews, sprint planning, and providing code-level guidance to ensure that team outputs align with defined design standards and enterprise security protocols.

Likely challenges to think through

• Balancing Innovation with Enterprise Governance

  A significant challenge is integrating cutting-edge agentic frameworks (e.g., CrewAI, LangGraph) into rigid enterprise environments. Prepare by articulating how you reconcile the need for rapid AI experimentation with strict corporate compliance, data security, and existing cloud governance policies.

• Managing Agentic Complexity and Security

  Agentic systems introduce unique risks like prompt injection, tool misuse, and non-deterministic behavior. Prepare to discuss your defense-in-depth strategies and how you architect systems to maintain control and security when agents interact with external tools or other agents via protocols like MCP.

Topics to prepare

• Agentic System Architecture

  Focus on designing multi-agent orchestration, task routing, and communication protocols. Exercise: Draft a high-level architecture diagram for a multi-agent system where agents must negotiate task delegation using a shared tool registry, ensuring you account for latency and failure handling.

• Advanced RAG Architectures

  Deepen understanding of Graph RAG, Vectorless RAG, and Hybrid RAG. Exercise: Compare the trade-offs between Graph RAG and Vectorless RAG for a scenario involving highly structured enterprise data, specifically focusing on retrieval accuracy and computational overhead.

• LLM Security and Defense-in-Depth

  Study prompt injection, jailbreaking, and data exfiltration risks within agentic workflows. Exercise: Design a security layer for an agentic application that validates tool-use chains and prevents unauthorized data access, applying defense-in-depth principles.

Skills to revise

• Agentic Framework Implementation

  Hands-on proficiency with LangGraph, CrewAI, or AutoGen. Exercise: Build a prototype using LangGraph that implements a stateful multi-agent workflow with a feedback loop for human-in-the-loop verification.

• LLMOps and Observability

  Mastering telemetry, monitoring, and evaluation using tools like LangSmith or Langfuse. Exercise: Configure a monitoring pipeline for a RAG application that tracks token usage, latency, and retrieval relevance scores to establish a production SLA.

• Cloud-Native AI Deployment

  Scaling AI solutions on Azure AI Foundry, Amazon Bedrock, or Google Vertex AI using Kubernetes. Exercise: Create a containerized deployment strategy for an agentic service that ensures cost-efficient scaling and compliance with enterprise governance policies.

• Develop a multi-agent architecture case study

  Prepare a technical walkthrough of a production-grade generative AI system you have built. Focus on how you implemented multi-agent orchestration, task routing, and tool-use chains using frameworks like LangGraph or CrewAI, specifically addressing how you managed latency and cost at scale.

• Demonstrate RAG and security expertise

  Be prepared to discuss your experience with advanced RAG architectures such as Graph RAG or Hybrid RAG. Detail your approach to implementing defense-in-depth strategies against LLM-specific security threats like prompt injection and jailbreaking within an enterprise environment.

• Map cloud-native AI deployment strategies

  Create a summary of your experience deploying AI solutions on Azure AI Foundry, Amazon Bedrock, or Google Vertex AI. Highlight your proficiency in containerization (Kubernetes/Docker) and how you established CI/CD pipelines and observability standards (e.g., LangSmith, Langfuse) for production-grade AI.

• Prepare for leadership and stakeholder discussions

  Formulate examples of how you have led engineering teams through architecture reviews and sprint planning. Be ready to explain how you translate complex business objectives into technical agentic requirements and manage stakeholder expectations during the transition from prototype to production.

• Formulate questions on enterprise AI governance

  Prepare thoughtful questions for the interviewers regarding how PwC manages AI interoperability protocols like MCP at scale, their specific approach to LLMOps for multi-region deployments, and how they balance innovation with strict enterprise compliance and governance policies.